Teams implementing OKRs for the first time often make the same decision: they start manually, using documents or spreadsheets, to understand how the process works in practice before introducing tools.
It’s a sensible instinct. OKRs are a habit before they’re a system. Jumping into software too early can add friction before teams understand what good execution looks like. At the same time, waiting too long can create problems that are difficult to unwind later.
The more useful question is not whether OKR software is needed, but when it meaningfully improves how the process runs.
Here are five questions worth answering before you make that call.
1. Do we understand why we’re running OKRs?
If the answer is “because leadership asked us to” or “because other companies do it,” software won’t fix that.
Before tools matter, clarity does:
- What problem are OKRs meant to solve for us?
- Alignment? Focus? Visibility? Accountability?
- What should be different at the end of the quarter if this works?
Teams that skip this step often end up with beautifully formatted OKRs that no one uses. Software makes intent visible - it doesn’t create it.
If you can’t explain why OKRs exist in your company yet, manual is fine.
2. Can we run a full OKR cycle without reminders?
Early on, the biggest failure mode isn’t bad goals. It’s abandonment.
Ask yourself:
- Are objectives being reviewed weekly?
- Do people remember to update progress without chasing?
- Are check-ins happening consistently?
If everything relies on one person nudging the team, software can help - but only once you’ve felt that pain firsthand.
Running at least one full cycle manually shows you:
- Where updates get skipped
- Where visibility breaks down
- Where ownership is unclear
That experience is what makes software valuable later.
3. Do we know what “good progress” actually looks like?
In early OKR cycles, teams often discover they’ve written:
- Activities instead of outcomes
- Binary goals with no signal
- Key Results that feel subjective
This isn’t a failure - it’s part of learning.
Before introducing a tool, ask:
- Can we tell mid-quarter if something is off track?
- Do we argue about status, or talk about outcomes?
- Are metrics helping decisions, or just reporting?
Software amplifies whatever signal you already have. It’s worth understanding your signal quality first.
4. Where is information already starting to fragment?
This is usually the turning point.
At some point, OKRs stop living in one place:
- Goals are in a doc
- Progress is in Slack updates
- Metrics are in spreadsheets or dashboards
- Context lives in meetings no one remembers
Manual systems work - until they don’t.
When teams start asking:
- “Which version is current?”
- “Where do I see this across teams?”
- “How does this connect to what we reviewed last quarter?”
That’s not a process problem. It’s a visibility problem. And that’s where software starts earning its keep.
5. Are we trying to learn - or trying to scale?
This is the most important question.
Manual OKRs are great for learning:
- Understanding the cadence
- Seeing how teams respond
- Figuring out what actually gets used
Software becomes useful when you want to scale:
- More team OKRs
- More dependencies
- More need for consistency and shared context
If you’re still experimenting, stay lightweight. If OKRs are becoming part of how you run the business, tools help turn them from an exercise into an operating rhythm.
A Quick Self-Check Before You Commit to OKR Software
Use the questions below as a short diagnostic. You don’t need perfect answers - but patterns matter.
You don’t “graduate” to software by checking every box. But when most of your answers drift to the right, manual systems usually start to creak.
Conclusion
Running OKRs manually at the start is often the right choice.
Early cycles are about learning how goals function in your organization: where ownership breaks down, which signals matter, and how consistently teams engage.
The challenge appears once OKRs begin influencing real decisions. As scope and dependencies grow, manual systems make progress harder to see and alignment harder to maintain. Information fragments, context gets lost, and feedback arrives too late to act on.
At that point, software isn’t overhead - it’s infrastructure. Not to add process, but to preserve clarity as execution scales.
